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  1. Nicole Seaman

    CFA Level 1 CFA: Correlation, covariance and probability topics

    Session 2, Reading 9 (Part 2): This video reviews portfolio variance and covariance, where covariance is the expected cross-product. We look at correlation, which is given by the covariance divided by the product of standard deviations, and therefore standardizes the covariance into a unitless...
  2. Nicole Seaman

    YouTube T2-8 Covariance: population vs. sample, and relationship to correlation

    Covariance is a measure of linear co-movement between variables. Independence implies zero covariance, but the converse is not necessarily true (because variables can be dependent in a non-linear way). Here is David's XLS: http://trtl.bz/2B9nqdO
  3. Nicole Seaman

    YouTube T2-4 What is statistical independence?

    Variables are independent if and only if (iff) their JOINT probability is equal to the product of their unconditional (aka, marginal) probabilities; i.e., if and only if Prob(X,Y) = Prob(X)*Prob(Y). Further, if variables are independent then their covariance (and correlation) is equal to zero...
  4. V

    R13-P1-T2- Miler Page 35 Question- Calculating Covariance & Correlation

    Can someone explain how mean & variance have been calculated in this example?
  5. Nicole Seaman

    P1.T2.711. Covariance and correlation (Miller, Ch.3)

    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an...
  6. kevolution

    Covariance matrix vs variance formula for 2-asset question

    I was looking at this specific 2-asset portfolio example and noticed that BT uses the matrix formula to get the variance of P. What I'm confused about is why do you not use the variance formula: variance = X1^2*stddev(asset1)^2 + X2^2*stddev(asset2)^2 +...
  7. Nicole Seaman

    P1.T2.706. Bivariate normal distribution (Hull)

    Learning objectives: Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating samples from a bivariate normal distribution. Describe properties of correlations between normally distributed variables when using...
  8. Nicole Seaman

    P1.T2.705. Correlation (Hull)

    Learning objective: Define correlation and covariance and differentiate between correlation and dependence. Questions: 705.1. In order to evaluate the the potential of a linear relationship between portfolio returns and a benchmark index, your colleague Richard conducted a univariate...
  9. Nicole Seaman

    P1.T2.506. Covariance stationary time series

    Learning outcomes: Define covariance stationary, autocovariance function, autocorrelation function, partial autocorrelation function and autoregression. Describe the requirements for a series to be covariance stationary. Explain the implications of working with models that are not covariance...
  10. Nicole Seaman

    P1.T2.502. Covariance updates with EWMA and GARCH(1,1) models

    Learning outcomes: Define correlation and covariance, differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH (1,1) models. Apply the consistency condition to covariance. Questions: 502.1. About the consistency condition, each of the following is true...